Although research in this area has shown that cramming different handcrafted features into neural networks might be sufficient to classify or identify speaker accent, the task of selecting the right input feature to apply remains non-trivial. As a result, it suffices to consider the use of a pretrained neural network-based acoustic representation as input feature. To this end, our study considers wav2vec [1]}: a model trained with a contrastive loss on large amounts of unlabeled audio data and with representations that improve several downstream classification tasks.
